1. Field of the Invention
This invention relates to methods for analysis of biological tissue sections; and more particularly to improved methods for quantitative pathology analysis across multiple consecutive tissue sections for a variety of applications.
2. Related Art
Quantitative pathology analysis includes a variety of known techniques for analysis of biological samples for the purposes of diagnosis and quantification of the presence of one or more diseases within a tissue specimen. For example, a pathologist might analyze a tissue sample to diagnose the presence of a cancer or other disease, or the pathologist might quantify the amount of disease present in a particular sample to determine a therapeutic dosage appropriate for treatment of the particular disease. The field of pathology was recently revolutionized with the emergence of digital imaging equipment. Prior to digital imaging, biological samples were viewed and analyzed under a microscope, and quantification of diseases was largely estimated by an experienced practitioner. Modernly, however, digital imaging equipment and related techniques are providing pathologists with tools for faster digital pathology analysis with improved accuracy in quantification methods.
With the ability to rapidly scan an entire tissue section on a glass microscope slide, pathologists are able to view and make critical histopathological determinations and/or diagnoses from a digital image rather than viewing under a microscope. A whole slide digital image also allows a pathologist to run quantitative image analysis across an entire tissue section or a designated region of interest (ROI) subset of the whole tissue section. However, this requires first either manually drawing digital regions of interest or running tissue analysis algorithms to determine the appropriate tissue locations where an image analysis routine should be run. This step is tedious and error-prone, whether it is done manually or with the use of a computer. And with each slide this process has to be repeated, further adding to increased error probability.
Immunohistochemistry (IHC) is a well established approach for observing protein or gene expression in tissue. It relies on a chromogen color change coupled to an antibody binding event. The pathologist scores IHC slides by selecting appropriate tissue areas on a glass slide under a microscope, or a computer monitor with a whole slide digital image, and then calculates a score. It has been well documented that the computer has higher precision than a pathologist at reproducibly quantifying colorimetric changes (Lange H, USCAP 2008, Consistent IHC HER2 Image Analysis on ScanScope Systems, Poster 006). This precision advantage unfortunately is handicapped by the extreme difficulty that the computer has in intelligently deciding on which region of a slide to make this colorimetric measurement. Many leading digital pathology software vendors are working on improved tissue analysis techniques, including but not limited to Visiopharm, Definiens, CRI, Bioimagene, and Aperio. A typical workflow involves a pathologist painting or outlining example specimens on one or more slides of each tissue type, and then the computer learns from these examples to create a solution that can be applied across an entire slide, identifying the tissue types outlined by the pathologists. This approach may work relatively well on a single slide, or sometimes can perform satisfactorily across many slides in a study where the histology and samples have been handled uniformly.
In practice, especially in oncology clinical trials, samples are collected under non-uniform conditions and with many uncontrolled variables of sample collection, fixation, and even immunohistochemistry processing. This makes the ability to run tissue analysis across non-uniform samples extremely difficult. A pathologist must either manually annotate regions of interest on every slide, or train tissue analysis solutions on each individual slide. Neither of these approaches are cost-effective, as pathologist time is one of the most costly in tissue diagnostics.
It would therefore be a benefit to provide improved methods for quantitative pathology analysis, whereby efficiency can be maximized by minimizing time spent by pathologists toward annotating multiple slides. Furthermore, it would be a significant advancement in the art if methods were developed for rapidly aligning and annotating regions of interest across several like samples, such as across multiple consecutive sections of biological tissue. In addition, it would be beneficial to provide methods for differential staining of adjacent tissue cross-sections, such that a plurality of scores across a multitude of inquiries may be obtained. Further benefits would include an ability to analyze adjacent cross-sections of tissue for a plurality of concerns, and to produce a hybrid score such that overall diagnosis and quantification can be improved. These methods may be embodied within one or more algorithms to be programmed in an image analysis system.